Mar 31
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This video introduces LLM Ops as the practice of managing the lifecycle of LLM application development, including prompt versioning, cost monitoring, tracing, and observability to prevent silent failures in production. Using a story about a RAG-based customer support bot, it shows how shipping new features without updating the knowledge base leads to outdated answers and churn, how prompt changes can introduce regressions like truncated troubleshooting steps without prompt versioning, and how missing token tracking and rate limits can cause costs to spiral. It explains how LLMs can fail silently and degrade over time, highlights challenges like non-determinism and “prompt is code,” and covers key LLM Ops concepts such as offline/online evaluations, drift and toxicity monitoring, RAG retrieval quality, deployment strategies (routing, fallbacks, caching, guardrails), and safety/governance including PII detection and audit logging. #llmops 00:00 What Is LLM Ops 00:51 Customer Support Bot Setup 02:23 Month Two Knowledge Drift 03:51 Month Three Prompt Regression 05:07 Month Four Cost Spiral 06:54 Why LLMs Fail Silently 08:12 Core Challenges Explained 11:38 Key Concepts Evaluation 12:35 Observability For RAG 13:55 Deployment Safety Governance 16:05 Wrap Up And Next Steps